AUTOMATED INFERENCE AND LEARNING IN MODELING FINANCIAL VOLATILITY

Abstract

This paper uses the specific-to-general methodological approach that is widely used in science, in which problems with existing theories are resolved as the need arises, to illustrate a number of important developments in the modeling of univariate and multivariate financial volatility. Some of the difficulties in analyzing time-varying univariate and multivariate conditional volatility and stochastic volatility include the number of parameters to be estimated and the computational complexities associated with multivariate conditional volatility models and both univariate and multivariate stochastic volatility models. For these reasons, among others, automated inference in its present state requires modifications and extensions for modeling in empirical financial econometrics. As a contribution to the development of automated inference in modeling volatility, 20 important issues in the specification, estimation, and testing of conditional and stochastic volatility models are discussed. A “potential for automation rating” (PAR) index and recommendations regarding the possibilities for automated inference in modeling financial volatility are given in each case.